Biological activity-based modeling identifies potential COVID-19 treatments



Computer-aided virtual screening is commonly used to sift through large collections of existing compounds for ones that have the potential to be useful drugs. Traditional virtual screening approaches rely on structural similarities between small molecules to infer possible cellular responses to the newly proposed compound. These structure-based approaches are often limited to identifying new drug candidates among compounds that have structures similar to those already known to be effective against a particular target, such as a viral protein.


IRP investigators led by Ruili Huang, Ph.D., developed a method for predicting how a compound will affect a particular target based on the activity of that compound in multiple prior biological tests. This method, called biological activity-based modeling (BABM), allows scientists to predict the compound’s effectiveness against a new target without knowing its structure. Thus, the BABM approach can identify promising potential therapeutics among a wider variety of molecules, including those without well-characterized structures. The approach was validated by identifying candidate antivirals for Zika and Ebola viruses based on data compiled from a specialized screening test that performed thousands of experiments simultaneously. In addition, the research team applied BABM models in order to identify around 100 compounds that showed activity against SARS-CoV-2, the novel coronavirus responsible for COVID-19.


BABM can quickly scan thousands of drug compounds to help identify new potential treatments for diseases such as COVID-19, with room for further development into anti-SARS-CoV-2 therapies. The general concept of BABM can be extended to any type of biological data, such as genomics, antibodies, and clinical data. As such, the BABM approach shows the promise of broad applications in different areas of biology. The data and source codes used by the IRP researchers are publicly available so that others can apply this approach to any collection of biological compounds.


Huang R, Xu M, Zhu H, Chen CZ, Zhu W, Lee EM, He S, Zhang L, Zhao J, Shamim K, Bougie D, Huang W, Xia M, Hall MD, Lo D, Simeonov A, Austin CP, Qiu X, Tang H, Zheng W. (2021). Biological activity-based modeling identifies antiviral leads against SARS-CoV-2. Nat. Biotechnol. Jun;39(6):747–753.

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This page was last updated on Thursday, June 8, 2023